Sung Algorithms in Bioinformatics

A Practical Introduction
1. Auflage 2009
ISBN: 978-1-4200-7034-7
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 407 Seiten

Reihe: Chapman & Hall/CRC Mathematical & Computational Biology

Thoroughly Describes Biological Applications, Computational Problems, and Various Algorithmic Solutions

Developed from the author’s own teaching material, Algorithms in Bioinformatics: A Practical Introduction provides an in-depth introduction to the algorithmic techniques applied in bioinformatics. For each topic, the author clearly details the biological motivation and precisely defines the corresponding computational problems. He also includes detailed examples to illustrate each algorithm and end-of-chapter exercises for students to familiarize themselves with the topics. Supplementary material is available at http://www.comp.nus.edu.sg/~ksung/algo_in_bioinfo/

This classroom-tested textbook begins with basic molecular biology concepts. It then describes ways to measure sequence similarity, presents simple applications of the suffix tree, and discusses the problem of searching sequence databases. After introducing methods for aligning multiple biological sequences and genomes, the text explores applications of the phylogenetic tree, methods for comparing phylogenetic trees, the problem of genome rearrangement, and the problem of motif finding. It also covers methods for predicting the secondary structure of RNA and for reconstructing the peptide sequence using mass spectrometry. The final chapter examines the computational problem related to population genetics.

Zielgruppe

Advanced undergraduate and beginning graduate students in bioinformatics or computational biology; mathematicians, computer scientists, statisticians, and biologists in computational biology or bioinformatics.

Autoren/Hrsg.

Sung, Wing-Kin

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie
• Naturwissenschaften Biowissenschaften Biowissenschaften
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle

Inhaltsverzeichnis

Introduction to Molecular Biology
DNA, RNA, Protein
Genome, Chromosome, and Gene
Replication and Mutation of DNA
Central Dogma (From DNA to Protein)
Post-Translation Modification (PTM)
Population Genetics
Basic Biotechnological Tools
Brief History of Bioinformatics
Sequence Similarity
Introduction
Global Alignment Problem
Local Alignment
Semi-Global Alignment
Gap Penalty
Scoring Function
Suffix Tree
Introduction
Suffix Tree
Simple Applications of Suffix Tree
Construction of Suffix Tree
Suffix Array
FM-Index
Approximate Searching Problem
Database Search
Introduction
Smith–Waterman Algorithm
FastA
BLAST
Variations of the BLAST Algorithm
Q-Gram Alignment Based on Suffix ARrays (QUASAR)
Locality-Sensitive Hashing
BWT-SW
Are Existing Database Searching Methods Sensitive Enough?
Multiple Sequence Alignment
Introduction
Formal Definition of Multiple Sequence Alignment Problem
Dynamic Programming Method
Center Star Method
Progressive Alignment Method
Iterative Method
Genome Alignment
Introduction
Maximum Unique Match (MUM)
Mutation Sensitive Alignment
Dot Plot for Visualizing the Alignment
Phylogeny Reconstruction
Introduction
Character-Based Phylogeny Reconstruction Algorithm
Distance-Based Phylogeny Reconstruction Algorithm
Bootstrapping
Can Tree Reconstruction Methods Infer the Correct Tree?
Phylogeny Comparison
Introduction
Similarity Measurement
Dissimilarity Measurements
Consensus Tree Problem
Genome Rearrangement
Introduction
Types of Genome Rearrangements
Computational Problems
Sorting Unsigned Permutation by Reversals
Sorting Signed Permutation by Reversals
Motif Finding
Introduction
Identifying Binding Regions of TFs
Motif Model
The Motif Finding Problem
Scanning for Known Motifs
Statistical Approaches
Combinatorial Approaches
Scoring Function
Motif Ensemble Methods
Can Motif Finders Discover the Correct Motifs?
Motif Finding Utilizing Additional Information
RNA Secondary Structure Prediction
Introduction
Obtaining RNA Secondary Structure Experimentally
RNA Structure Prediction Based on Sequence Only
Structure Prediction with the Assumption That There Is No Pseudoknot
Nussinov Folding Algorithm
ZUKER Algorithm
Structure Prediction with Pseudoknots
Peptide Sequencing
Introduction
Obtaining the Mass Spectrum of a Peptide
Modeling the Mass Spectrum of a Fragmented Peptide
De novo Peptide Sequencing Using Dynamic Programming
De novo Sequencing Using Graph-Based Approach
Peptide Sequencing via Database Search
Population Genetics
Introduction
Hardy–Weinberg Equilibrium
Linkage Disequilibrium
Genotype Phasing
Tag SNP Selection
Association Study
References
Index
Exercises appear at the end of each chapter.

Über Autor(innen)

Wing-Kin Sung is an associate professor at the National University of Singapore.